The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler–Deb–Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

This project proposes the design of a wireless body area network (WBAN) microstrip patch antenna covered by the jeans fabric as the outer layer operating at the center frequency, f_c of 2.40 GHz. Precisely, the microstrip patch antenna with the inset-fed edge technique is designed and simulated systematically by using the Keysight Advanced Design System (ADS) software where the FR4 board with the dielectric constant, _r of 4.70, dissipation factor or loss tangent, tan δ of 0.02 and height, h of 1.60 mm is the chosen dielectric substrate. The wearable microstrip patch antenna design is then fabricated using the FR4 printed circuit board (PCB) material, hidden inside the jeans fabric, and attached to clothing, such as a jacket accordingly. Simulation and fabrication measurement results show that the designed microstrip patch antenna characteristics can be applied significantly within the industrial, scientific, and medical (ISM) radio band, which is at f_c = 2.40 GHz.

In this study, we proposed a bio-inspired technique known as modified cuckoo search (MCS)-based weighted sum algorithm towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL) and/or nulls control. The newly evolved metaheuristic algorithm was primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. Through the integration with the Roulette wheel selection operator and the inertia weight controlling the position (solution) exploration, the MCS-based weighted sum approach optimized concurrently the array element excitation locations, amplitudes, and phases within the uniform pattern and Dolph-Chebyshev window, respectively. The optimal solutions obtained were then compared against the conventional (with λ/2 inter-element distance) and other chosen evolutionary algorithms-based arrays.

Finite length of sequences that are modulated both in phase and amplitude and have an ideal autocorrelation function (ACF) consisting of merely a pulse have many applications in control and communication systems. They are widely applied in control and communication systems, such as in pulse compression systems for radar and deep-space ranging problems [1-5]. In radar design, the important part is to choose a waveform, which is suitable to be transmitted because the waveform controls resolution in clutter performance. In addition, it can solve a general signal problem particularly related to the digital processing. Energy ratio (ER), total side lobe energy (SLE), and peak sidelobe level (PSL) are three properties of such sequences interest. This paper presents a method using the Complementation, Cyclic Shift and Bit Addition for synthesizing and optimizing a binary sequence implemented to improve the sequences of a similar quality with the Barker sequence, particularly for lengths greater than 13. All of these methods are guided by the specific parameter with good characteristics in ACF (ER, SLE, and PSL) [6,7,8]. Such sequences can then be effectively used to improve the range and Doppler resolution of radars.

Breast cancer is a frightening type of cancer for women globally, including in Indonesia. Early detection of cancer symptoms is crucial to reducing mortality rates. Microwave imaging systems using the ultra-wideband (UWB) can detect objects, particularly in medical diagnosis. The textile antenna is the latest development in antenna technology that can be used on the human body. This antenna can be bent and adjusted to the body's complex shape. This research proposed a textile antenna using the felt dielectric substrate material operating at 3.8 GHz. The simulation results showed a return loss value of -47.5138 dB and a bandwidth of 1.4 GHz. The antenna was also tested on a breast phantom with a tumour scenario, showing higher amplitude in the quadrant with the tumour. The fabrication was done manually through laser cutting and heat-assisted bonding. The antenna made of the felt substrate material could detect breast tumours’ multiple depths or layers using the graphical user interface (GUI) programming.