Search

Economic dispatch (ED) problem is a popular optimization problem in electrical engineering. Many studies that handled ED problem employed metaheuristics as the optimization technique. Unfortunately, these metaheuristics are old or the existing ones. Meanwhile, ED problem is not the favourite constrained case in studies that introduced a novel metaheuristic. This research introduces a novel metaheuristic called quadratic time optimization (QTO). QTO comprises three sequential searches where both diversification and intensification are blended in every search. But the portion of both orientations changes by following the quadratic time. The appraisal of QTO is performed by employing it to solve both unconstrained and constrained problems. The 23 traditional functions represent the unconstrained problem while economic load dispatch (ELD) problem with valve point effect and spinning reserve problem represents the constrained problem. In both appraisals, there are five novel metaheuristics represent the benchmarks, including: walrus optimization algorithm (WaOA), coati optimization algorithm (COA), total interaction algorithm (TIA), addax optimization algorithm (AOA), and language education optimization (LEO). The result shows the competitiveness of the proposed QTO in handling both unconstrained and constrained problems. QTO becomes the first best in 8 functions out of 23 functions including five high dimension unimodal functions, two high dimension multimodal functions, and one fixed dimension multimodal functions. The result also shows that QTO is competitive in three use cases in ELD problems as the ratio between the range and average total cost among metaheuristics for the first, second, and third cases is 0.72, 0.04, and 0.02 percent consecutively. [ABSTRACT FROM AUTHOR]

As stated in the no-free-lunch (NFL) theory, there is not any optimizer suitable for all problems. This circumstance becomes the motivation of introducing a new swarm-based metaheuristic called best couple algorithm (BCA). BCA is constructed as a swarm-based metaheuristic where the swarm is split into two sub-swarms. There are two types of splitting. The first split is dividing the swarm into the first half and second half of swarms. The second split is dividing the swarm into the odd indexed swarm members and even indexed swarm members. There is a sub swarm leader representing the highest quality swarm member in every sub swarm. There are two sequential searches for every split: the motion toward the middle between two sub swarm leaders and the motion relative to the middle between two randomly picked sub swarm members. In the benchmark assessment, BCA is compared with total interaction algorithm (TIA), coati optimization algorithm (COA), language education algorithm (LEO), osprey optimization algorithm (OOA), and walrus optimization algorithm (WaOA). The result shows that BCA is superior to these five contenders as it is better than TIA, COA, LEO, OOA, and WaOA in 18, 18, 16, 18, and 18 functions respectively out of 23 functions. [ABSTRACT FROM AUTHOR]

In the recent years, there are a lot of swarm-based stochastic optimization techniques i.e., metaheuristics were introduced. Most of these techniques were tested to solve the sets of theoretical functions. Some of them were enriched with practical tests where the common use cases are the mechanical engineering designs. On the other hand, the similar studies that utilized the optimization in power system are difficult to find. Moreover, the environmental issues become major considerations in engineering field. Based on this evidence, this paper constructs a new swarmbased optimization technique called stay-jump optimizer (SJO). The equal size swarm split is applied in the beginning of the process. Then, two directed searches toward the highest quality sub-swam members and two randomly selected higher quality sub swarm members are employed. The performance investigation is performed by employing SJO to find the optimal solution of 23 classic functions and the economic emission dispatch (EED) problem. The use-case for EED is the Java-Bali power grid system in Indonesia that consists of six steam power plants and two hydro-electric power plants. Five new optimization techniques including addax optimization algorithm (AOA), dollmaker optimization algorithm (DOA), giant armadillo optimization algorithm (GAO), zebra optimization algorithm (ZOA), and total interaction algorithm (TIA). The result shows that SJO is superior to its opponents as it is better than AOA, DOA, GAO, ZOA, and TIA in 17, 17, 16, 19, and 14 functions out of 23 functions respectively. SJO also becomes the best in the EED problem. [ABSTRACT FROM AUTHOR]

Metaheuristic has been utilized extensively to optimize power system. Meanwhile, the no-free-lunch (NFL) theory becomes the major consideration in the massive development of metaheuristic as there is not any ideal metaheuristic can solve all problems superiorly. Based on this problem, this work is aimed at introducing a novel metaphorfree swarm-based metaheuristic called iteration-controlled mixture optimizer (ICMO). ICMO contains three directed searches where the reference in each search is constructed by two entities. The first reference is the mixture between the finest entity and the mean of the finer entities. The second reference is the mixture between the finest entity and a randomly chosen entity. The third reference is the mixture between the finest entity and any generated entity within space. The portion between the first and second entities in each reference is controlled by the iteration. Then, ICMO is compared with five new swarm-based metaheuristics: attack leave optimization (ALO), total interaction algorithm (TIA), fully informed search algorithm (FISA), walrus optimization algorithm (WaOA), and ono-to-one based optimization (OOBO). The assessment result shows that ICMO is better than ALO, TIA, FISA, WaOA, and OOBO in 15, 13, 20, 12, and 20 functions out of 23 functions respectively. Then, ICMO is also challenged to solve the economic load dispatch (ELD) problem in the Java-Bali electricity system in Indonesia. The result shows that ICMO is competitive compared to these five metaheuristics in solving this practical problem. The result shows that the range between the best and worst metaheuristics in this problem is narrow as it represents the integer-based problem. [ABSTRACT FROM AUTHOR]

This article introduces a novel stochastic optimization method termed the Best-Other Algorithm (BOA). The nomenclature reflects its reliance on the best member, which is amalgamated with other entities. BOA, a metaphor-free swarm-based metaheuristic, comprises three directed searches. The first involves subtracting the best member from a randomly selected member. The second entails determining the midpoint between the best member, and another randomly chosen member. The third centers around the midpoint between the best member and a random solution along the space. The efficacy of BOA is evaluated by challenging it to solve a collection of 23 functions. In this evaluation, BOA is pitted against five other metaheuristics: Northern Goshawk Optimization (NGO), Zebra Optimization Algorithm (ZOA), Coati Optimization Algorithm (COA), Migration Algorithm (MA), and Osprey Optimization Algorithm (OOA). The findings indicate the superiority of BOA over its counterparts. BOA outperforms NGO, ZOA, COA, MA, and OOA in 21, 15, 16, 15, and 17 functions, respectively. These results underscore the pivotal role of the best member as a reference and the comparatively lesser significance of the neighborhood search as the search space diminishes during the iteration. [ABSTRACT FROM AUTHOR]

This paper promotes a new metaheuristic called the half mirror algorithm (HMA). As its name suggests, HMA offers a new kind of mirroring search. HMA is developed by hybridizing swarm intelligence and the evolution system. Swarm intelligence is adopted by constructing several autonomous agents called swarms. On the other hand, the evolution system is adopted using arithmetic crossover based on a particular reference called a mirror. Four mirrors are used in HMA: the best swarm member, a randomly selected swarm member, the central point of the space, and the corresponding swarm member. During the confrontative assessment, HMA is confronted with average and subtraction-based optimization (ASBO), total interaction algorithm (TIA), walrus optimization algorithm (WaOA), coati optimization algorithm (COA), and clouded leopard optimization (CLO). The result shows that HMA is superior to ASBO, TIA, WaOA, COA, and CLO in 20, 19, 19, 20, and 20 out of 23 functions, respectively. Moreover, HMA has found the global optimal of eight functions. It means the superiority of HMA occurs in almost entire functions. In the future, the mirroring search can be combined with the guided and neighborhood search to construct a more powerful metaheuristic. [ABSTRACT FROM AUTHOR]

There are two problems in the development of swarm-based metaheuristic. First, there are not any metaheuristic is able to solve all problems superiorly. Second, the cross-dimension mapping between the entity and its reference during performing the directed search is rare to find. Based on these problems, this work introduces a new metaphorfree swarm-based metaheuristic called the triple-shake algorithm (TSA). As its name suggests, TSA consists of three directed searches. The reference in the first search is the balance mixture between the finest entity and the member of permutation set. The reference in the second search is the balance mixture between the finest entity and a randomly chosen entity. The reference in the third search is the finest entity only. But the cross-dimension mapping is performed in this third search with 50 percent probability. In the benchmark assessment, TSA is compared with zebra optimization algorithm (ZOA), walrus optimization algorithm (WaOA), migration algorithm (MA), total interaction algorithm (TIA), and one-toone based optimization (OOBO). The result indicates the dominance of TSA among its comparators. TSA is better than ZOA, WaOA, MA, TIA, and OOBO in 21, 19, 17, 19, and 22 functions consecutively out of 23 functions. [ABSTRACT FROM AUTHOR]

The no-free-lunch (NFL) theory has become the main reason of developing new metaheuristics in decades. Besides, the strict dimension mapping has been implemented in many population-based metaheuristics, especially the swarm-based metaheuristics. Regarding this problem, this paper proposes a new swarm-based metaheuristics called focus and shake algorithm (FSA). FSA has novel approach in the dimension mapping between the agent and its reference during the directed motion. It combines the strict dimension mapping called as focus approach and the randomized dimension mapping called as shake approach to enhance its exploration ability. FSA deploys two directed motions based on two references. The first reference is constructed based on the balance mixture between two finer agents while the second reference is constructed based on the balance mixture of the finest agent and a randomly picked agent. In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19, 21, 21, 19, and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems. In the future, the cross-dimension mapping can be more explored to develop finer swarm-based metaheuristics. [ABSTRACT FROM AUTHOR]

This paper introduces a novel metaheuristic named the stochastic shaking algorithm (SSA), which is rooted in swarm intelligence principles. The innovation lies in its unique utilization of iteration for selecting references during guided searches through a stochastic approach. The optimization process involves two sequential steps: the primary reference in the first step is the finest swarm member, while in the second step, it is the mean of all finer swarm members plus the finest one. This primary reference is then combined with a randomly chosen solution within the space, serving as the secondary reference. SSA undergoes evaluation in two contexts. The first involves assessing its performance using a set of 23 classic functions as a theoretical use case. The second involves tackling the economic load dispatch problem (ELD), a practical use case featuring a system with 13 generators of various energy resources. The study compares SSA against five other metaheuristics--One to One Based Optimization (OOBO), Kookaburra Optimization Algorithm (KOA), Language Education Optimization (LEO), Total Interaction Algorithm (TIA), and Walrus Optimization Algorithm (WaOA). Results indicate SSA's superiority over OOBO, KOA, LEO, TIA, and WaOA in 21, 13, 11, 16, and 14 functions out of 23 functions, respectively. Additionally, the evaluation of the economic load dispatch problem reveals intense competition among the six metaheuristics. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions. [ABSTRACT FROM AUTHOR]

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21, 20, 17, 17, and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions. [ABSTRACT FROM AUTHOR]

This paper presents a new metaphor-free metaheuristic search called the swarm bipolar algorithm (SBA). SBA is developed mainly based on the non-free-lunch (NFL) doctrine, which mentions the non-existence of any general optimizer appropriate to answer all varieties of problems. The construction of SBA is based on splitting the swarm into two equal-sized swarms to diversify the searching process while performing intensification within the subswarms. There are two types of finest swarm members: the finest swarm member for the whole swarm and the finest swarm member in every sub-swarm. There are four directed searches performed in every iteration: (1) search toward the finest swarm member, (2) search toward the finest sub-swarm member, (3) search toward the middle between two finest sub-swarm members, and (4) search relative to the randomly picked swarm member from another sub-swarm. The performance of SBA is assessed through two assessments with a set of 23 functions representing the optimization problem. In the benchmark assessment, SBA is contended with five metaheuristics: northern goshawk optimization (NGO), language education optimization (LEO), coati optimization algorithm (COA), fully informed search algorithm (FISA), and total interaction algorithm (TIA). The result presents the superiority of SBA among its contenders by being better than NGO, LEO, COA, FISA, and TIA in 21, 16, 16, 21, and 18 functions. The single search assessment is performed to evaluate each strategy involved in SBA. The result shows that the search toward the middle between the two finest sub-swarm members is the best among the four searches in SBA. [ABSTRACT FROM AUTHOR]

This work presents a new metaheuristic called treble opposite algorithm (TOA). It consists of three phases. There are two searches that are opposite to each other performed in each phase. In the first phase, the search toward and away from the best solution is carried out. In the second phase, the search toward and away from the middle between two randomly picked solutions is carried out. In the third phase, a neighborhood search around the narrow and large space is carried out. A candidate is selected among the two searches in every phase. TOA is challenged to solve theoretical and practical problems. The 23 functions represent theoretical problems, while the portfolio optimization of stocks in the banking sector listed in IDX30 represents the practical problem. TOA is compared with five metaheuristics: grey wolf optimization (GWO), golden search optimization (GSO), average subtraction-based optimization (ASBO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The result indicates that TOA is superior to its competitors as it is better than GWO, GSO, ZOA, ASBO, and COA in 22, 23, 19, 20, and 19 functions respectively, in handling 23 functions and produces the highest total capital gain in handling portfolio optimization problem. In the future, TOA can be utilized to handle many other realworld optimization problems. Moreover, TOA can be hybridized with other metaheuristics to improve its performance. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

There has been a massive development of metaheuristic algorithms in the latest decade where swarm intelligence becomes the fundamental approach. Meanwhile, there is still no ideal metaheuristic that can solve all problems superiorly, as declared in the no-free-lunch (NFL) theory. This work introduces a novel swarm-based metaheuristic named as migration-crossover algorithm (MCA). In MCA, the swarm intelligence is enriched with the crossover technique and the neighbourhood search with unbalanced local search space. The global finest solution becomes the reference in the first step while the middle between two stochastically chosen solutions becomes the reference in the second step. The neighbourhood search is performed in the third step. The collection of 23 functions become the use case during the evaluation of MCA. In the first evaluation, MCA is compared with five new metaheuristics: total interaction algorithm (TIA), osprey optimization algorithm (OOA), migration algorithm (MA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA). The result reveals that MCA is finer than TIA, OOA, MA, COA, and WaOA in 20, 19, 17, 20, and 17 functions subsequently. The result of the second evaluation reveals that the global finest solution becomes the dominant contributor in the high dimension functions while the middle between two stochastically chosen solutions becomes the dominant contributor in the fixed dimension functions. [ABSTRACT FROM AUTHOR]

This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions. [ABSTRACT FROM AUTHOR]

Migration algorithm (MA) and walrus optimization algorithm (WaOA) are two new swarm-based metaheuristics which are first introduced in 2023. As new metaheuristics, the modification of these two metaheuristics is still rare. Based on this circumstance, this work constructs a new metaheuristic called as migrating walrus algorithm (MWA) based on the hybridization of both MA and WaOA to create a better metaheuristic than them. MWA consists of five migrations: four directed migrations and one local migration. The references used in these directed migrations are the best walrus, a randomly picked better walrus, a randomly picked walrus, and two randomly picked walruses. In this work, two assessments are carried out: the comparative assessment and the individual migration assessment. The 23 functions are selected as theoretical use cases. In the comparative assessment, MWA is confronted with five new metaheuristics: MA, WaOA, attack leave optimization (ALO), coati optimization algorithm (COA), and osprey optimization algorithm (OOA). The result shows that MWA is better than ALO, COA, MA, OOA, and WaOA in 19, 19, 19, 17, and 17 functions. On the other hand, the individual migration assessment result indicates that the multiple migration approach is important to maintain the superiority of MWA with the directed migration toward the best walrus becoming the most important contributor. This result also strengthens the necessity of the multiple searches strategy rather than a single strategy. [ABSTRACT FROM AUTHOR]

This study designs a new stochastic optimization i.e., metaheuristic technique, namely run-catch optimizer (RCO). RCO provides a distinct mechanism regarding the diversification-intensification strategy. Each member runs two sequential activities in every iteration. The first activity is running and the second one is catching. Each activity generates a seed. In the first activity, a virtual best member moves away from the corresponding member to become the first seed. The second seed is generated along the way between the corresponding member and the first seed in the first activity. If both seeds fail to improve, the member conducts a random search to find a new member. Otherwise, the better seed replaces the corresponding member. Then, RCO is challenged to handle both theoretical and real-world optimization problems. The classic 23 functions represent theoretical problems, while the outsourcing optimization problem represents the practical problem. In these simulations, RCO is confronted with five other algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that RCO is better than POA, KMA, MPA, GWO and PSO in optimizing 20, 21, 13, 12, and 21 functions consecutively. Meanwhile, RCO is better than PSO, GWO, and KMA, but worse than MPA and POA in optimizing the outsourcing problem. [ABSTRACT FROM AUTHOR]

This paper promotes a new swarm-based metaheuristic called four directed search algorithm or FDSA. FDSA is designed as a directed search-based metaheuristic without deploying any neighbourhood search. It contains four directed searches that are carried out sequentially. Each search has its reference. These four references are the best member; the resultant of three shuffled members within the swarm; a shuffled member within the swarm; and the resultant of the best member, a shuffled member within the swarm, and the corresponding member. FDSA implements a strict acceptance procedure so that new solution is accepted only if it provides improvement. The investigation is carried out to evaluate the performance of FDSA in solving the 23 functions. FDSA is also confronted with five new metaheuristics: northern goshawk optimization (NGO), average subtraction-based optimization (ASBO), coati optimization algorithm (COA), mixed leader-based optimization (MLBO), and attack leave optimization (ALO). This work also investigates the contribution or dominance of each search in the context of finding the optimal solution. The result shows that FDSA is superior to all these confronters by consecutively outperforming NGO, ASBO, COA, MLBO, and ALO in the 17, 13, 11, 18, and 11 functions. Its superiority is mainly in the high-dimension functions. Through investigation, there is no dominant search among the four directed searches in FDSA. Meanwhile, multiple search strategy is proven to improve performance significantly. On the other hand, the contribution of neighbourhood search is not significant. [ABSTRACT FROM AUTHOR]

This work offers a new stochastic optimization i.e., a metaheuristic algorithm combining both direction-based search and neighbourhood search called as walk-spread algorithm (WSA). These two types of searches become the inspiration for its name where the term walk represents the direction-based search while the term spread represents the neighbourhood search. There are two direction-based searches performed in every iteration where each search produces a single child. Meanwhile, there are two neighbourhood searches performed in every iteration where each search produces several children. The global best unit becomes the first reference while two shuffled units become the second reference in performing the direction-based search. Meanwhile, the local search space of the first neighbourhood search is wide while the second one is narrow. The 23 classic functions are chosen as the assessment of WSA where WSA is confronted with the five latest metaheuristics: mixed leader-based optimization (MLBO), golden search optimization (GSO), pelican optimization algorithm (POA), zebra optimization algorithm (ZOA), and attack-leave optimization (ALO). The assessment result shows that the offered WSA achieves the acceptable result so fast. Moreover, WSA is also superior to these five confronters by outperforming MLBO, GSO, POA, ZOA, and ALO in 23, 23, 22, 21, and 21 functions respectively. [ABSTRACT FROM AUTHOR]

This study was conducted to promote a new adaptive cone algorithm (ACA) algorithm. ACA is a metaheuristic technique based on swarm intelligence. ACA contains three steps. Each agent moves closer to the global reference in the first step. Then, each agent searches for a better solution around the current solution in the second step. The global reference searches for better solutions around it in the third step. This algorithm is named cone because the local space size declines linearly during the iterative process. ACA introduces a new adaptability model to improve the exploration strategy when a better solution cannot be achieved. It is conducted by enlarging the local solution space. ACA is challenged to find the final solution for theoretical and practical problems. The 23 functions are chosen as theoretical optimization problems. The portfolio optimization problem is selected as the practical problem. ACA is compared with five algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), marine predator optimization (MPA), average subtraction-based optimizer (ASBO), and pelican optimization algorithm (POA). The result shows that ACA is competitive in finding the optimal solution for 23 functions and outperforms all sparing algorithms in achieving the highest total capital gain in tackling the portfolio optimization problem. ACA is superior to PSO, GWO, MPA, ASBO, and POA in solving 20, 11, 13, 4, and 21 functions, respectively. In the future, ACA can be implemented in solving various practical optimization problems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel swarm-based metaheuristic called swarm magnetic optimizer (SMO). SMO imitates the behaviour of two magnets close to each other: pushing toward or pulling away from each other. This pushpull mechanism is then adopted to become a novel search in SMO. SMO is set up as a swarm of magnets that move autonomously. In the early iteration, the pull strategy is dominant. Meanwhile, it declines during the iteration and is replaced by a push strategy. The determination between these two strategies is calculated stochastically. SMO consists of three sequential phases in every iteration where the corresponding magnet and its reference run a push or pull search in each phase. The reference used in each phase is the global best magnet, a randomly selected magnet, and a randomly generated magnet. This paper evaluates SMO through simulation to find the optimal solution for 23 functions. The performance of SMO is compared with five latest metaheuristics: mixed leader-based optimizer (MLBO), guided pelican algorithm (GPA), zebra optimization algorithm (ZOA), coati optimization algorithm (COA), and clouded leopard optimizer (CLO). The result shows that SMO is better than MLBO, GPA, ZOA, COA, and CLO in consecutively 22, 15, 11, 13, and 14 functions. The superiority of SMO, especially, is in solving high dimension functions. Meanwhile, in the fixed-dimension multimodal functions, SMO is still superior to MLBO but less superior to GPA, ZOA, COA, and CLO. [ABSTRACT FROM AUTHOR]

A new swarm intelligence-based metaheuristic optimizer, namely Partial Leader Optimizer (PLO), is presented. PLO contains several autonomous agents that represent the solution. The best solution represents collective intelligence, i.e., the leader. PLO has distinct mechanics in finding the acceptable solution during the given iteration. Every agent moves to a specified target in every iteration. Two options can be chosen to determine the target. First, the target is calculated by pushing the virtual best solution away from the corresponding agent. Second, the target is randomly chosen within the solution space. This target selection is conducted stochastically based on the threshold that is set manually before the iteration. Then, several candidates are generated between the target and the agent's current location. The distance between adjacent candidates is the same. The agent moves to the best candidate and updates the best solution. Simulation is implemented to observe and analyze the PLO's performance. The well-known 23 benchmark functions are used as the optimization problems. In this simulation, PLO is benchmarked with marine predator algorithm (MPA), particle swarm optimization (PSO), average subtraction-based optimizer (ASBO), slime mold algorithm (SMA), and pelican optimization algorithm (POA). The result shows that PLO is competitive compared to these algorithms, especially in solving fixeddimension multimodal functions. PLO is better than PSO, MPA, SMA, ASBO, and POA in optimizing 22, 19, 18, 9, and 20 functions out of 23, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The central concept of ALO is to intensify guided searches as a required method. Then, a random search is performed only if the guided search fails to improve the current solution. ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional. In the first phase, the guided search is conducted with the best global solution as the reference. In the second phase, the guided search is conducted with a randomly selected solution as the reference. The random search is performed in the third phase. Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic. [ABSTRACT FROM AUTHOR]